Emotion and Efficacy Pathways to Normative and Non-normative Collective Action
Normative Agents in Health Care [email protected] Normative AgentsHealth Care Normative Agents in...
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[email protected] Normative Agents in Health Care Normative Agents in Health Care Normative Agents Normative Agents in Health Care Health Care: Uses and Challenges Javier Vazquez-Salceda Utrecht University http://www.cs.uu.nl/people/javier Invited talk Invited talk
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Transcript of Normative Agents in Health Care [email protected] Normative AgentsHealth Care Normative Agents in...
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Normative AgentsNormative Agents in Health CareHealth Care:Uses and Challenges
Javier Vazquez-Salceda
Utrecht University
http://www.cs.uu.nl/people/javier
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Motivation
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Motivation (I)
• New environment for Health Care services– Need to promote innovative HC services
– patient-centered services
– inter-connectivity
• the European e-Health Area
Aims:• improve patient care• more efficient & responsive HC servicesMeans:• integrate EU health policies• concentrate resources• avoid duplicity of effort[EU Health Strategy, 2000][EU Health Strategy, 2000]
Target IST´s:• European electronic HC card• EU Heath Information Networks • On-line services
• info on illness prevention• teleconsultation• electronic records• e-reimbursement
[eEurope 2005 priorities, 2002][eEurope 2005 priorities, 2002]
PatientPatientMobilityMobility
[Health Council report,[Health Council report,December 2003]December 2003]
PatientPatientMobilityMobility
[Health Council report,[Health Council report,December 2003]December 2003]
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Motivation (II)Application in a distributed, highly regulated eHealth environment
• Distributed software solutionsDistributed software solutions should address:
– Data exchange problem:
– Communication problem:
– Coordination issues:
– Variety of regulations:
– Trust:
standard data interchangeformats
international notations or translation mechanisms
policies,planners,shared dietaries.
?
Agent CommunicationLanguages
Agent-Mediated Coordination
Agent-Mediated Electronic Institutions
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Case Study (I)
• Distributed organ and tissue allocation.
• 2 kinds of transplants:
– organs
• You can not conserve them on banks
• Every new organ donation (manual) search for the recipient
– tissues
• You can keep them on banks, (not very long)
• Every new recipient (manual) search for tissue
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Case Study (II)• Organ and tissue allocation not only a national, but a trans-national
problem– Scarcity of donors led to international coalitions
• United Network for Organ Sharing (USA)
• EUROTRANSPLANT (AS, B, D, LUX, NL, Slovenia)
• Scandiatransplant (Denmark, Finland, Iceland, Norway, Sweden)
• Donor Action Foundation (USA, Spain, EUROTRANSPLANT)
– Variety of regulations
• EU projects only cover data format or networking problems– RETRANSPLANT, TECN (data formats, distributed DB)
– ESCULAPE (tissue histocompatibility)
• Other MAS for organ allocation [Callisti et al], [Moreno et al] do not cover the normative dimension
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Contents
• A Language for Norms
• Normative Agents
• Norms and Agent Platforms: Electronic Institutions
• Conclusions and Challenges
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A Language for Norms
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Representing Norms (I)
• Formal representation of norms needed
• Which logic?
– Norms permit, oblige or prohibit
– Norms may be conditional
– Norms may have temporal aspects
– Norms are relativized to roles
variant of Deontic Logic
OBLIGED, PERMITTED, FORBIDDEN
IF C
BEFORE D, AFTER D
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Representing Norms (II) • Type 1: Unconditional norms about predicates
– the norms on the value of P are active at all times:
– an example:
• Type 2: Unconditional norms about actions– the norms on the execution of A are active at all times:
– an example:
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Representing Norms (III)
• Type 3: Conditional norms
– the activation of the norms is conditional under C– C may be a predicate about the system or the state of an action:
– an example:
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Representing Norms (IV)
• Type 4: Conditional norms with Deadlines
– the activation of norms is defined by a deadline
– absolute and relative deadlines:
– an example:
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Representing Norms (V)
• Type 5: Obligations of enforcement of norms
– norms concerning agent b generate obligations on agent a:
– an example:
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Norms and Agents
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• Medicine is a very sensible domain– We mush ensure proper behaviour of agents
– Agents should keep a certain autonomy
• We can express agents´ acceptable behaviour with norms
– WARNING: it is not straight-forward!
Normative Agents (I)Ensuring proper agent behaviour with norms
Agents
AutonomyAutonomy VS
ControlControl
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Normative Agents (II)
• We should first analyse the impact of norms on cognitive agents
• Our norms are expressed in deontic logic with proper Kripke semantics
– Kripke model of the impact of norms
– Possible worlds
• Our model is composed by 2 dimensions
– Epistemic dimensionEpistemic dimension (states and behaviours as Possible Worlds)
– Normative dimensionNormative dimension (norms applying to the agent)
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Normative Agents (III)
legal
illegal
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Normative Agents (IV)Safety and Soundness
• The concept of legally accessible worlds allows to describe – wanted (legal) and
unwanted (illegal) behaviour
– acceptable (safe) and unnacceptable (unsafe) states
• ViolationsViolations when agents breaks one or more norms, entering in an illegal (unsafe) state.
• SanctionsSanctions are actions to make agents become legal (safe) again.
• Sanctions include the actions to recover the system from a violation
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Normative Agents (V)Context
• In real domains norms are not universally valid but bounded to a given context.– HC norms bounded to trans-national, national and
regional contexts
• A Context Context is a set of worlds with a shared vocabulary and a normative framework– e-instX is a context defining a ontology and
a normative specification
• Usually nested contextsnested contexts
– there are super-contexts that have an influence in e-instX ontology and norms
• Special impact on the Ontologies– Proposal: not to force a single representation for all
contexts, but interconnected ontologies (multi-contextual ontologies).
Cn
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Normative Agents (VI)
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Implementing Normative Agents (I) Influence of norms in the BDI deliberation cycle
percepts actionsbeliefs
desiresplans
intentions
norms (obligations,
permissions...)
(joint)
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Implementing Normative Agents (II) Operationalization of Norms
Norms should guide the behaviour of the Agent
Problems:
Norms are more abstract than the procedures
Norms do not have operational semantics
Example:
Regulation: “It is forbidden to discriminate potential recipients of an organ based on their age (race, religion,...)”
Formal norm: FORBIDDEN(discriminate(x,y,age))
Procedure: does not contain action “discriminate”
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Implementing Normative Agents (III) Standard BDI interpreter
Problems:
• too simple
• there is no new perception until the previous plan has been executed
• overcommitment
• no support for norms
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Implementing Normative Agents (IV) Extending the BDI interpreter with norms
options considers also the obligation eventsimposing new actions
reconsider decides when to check intentions andaction plans
sound checks if plan is still applicable. Avoids overcommitment to plans
filter restricts unwantedactions. Checks not onlyfeasibility but alsolegal allowance.
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Norms in Agent Platforms:
Electronic Institutions
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Electronic Institutions (I)
• Need of a safe environment where proper behaviour is enforced.
• InstitutionsInstitutions are a kind of social structure where a corpora of constraints (the institution) shape the behaviour of the members of a group (the organization)
• An e-Institutione-Institution is the computational model of an institution through the specification of its normsnorms in (some) suitable formalism(s). In the context of MAS they:– reduce uncertaintyuncertainty of other agents’ behaviour– reduce misunderstandingmisunderstanding in interaction– allows agents to foresee the outcomeforesee the outcome of an interaction– simplify the decision-makingdecision-making (reduce the possible actions)
– Agent behaviour guided by Normsbehaviour guided by Norms
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Statutes (values,objectives,context) Model Ontology
OrganizationalModel
SocialModel
InteractionModel
Norm level
Rule level
NormativeImplementation
GenericComm.Acts
ConcreteDomain
Ontology
SpecificComm.
Acts
ProceduralDomain
Ontology
Normative Dimension Organizational Dimension
Ontological Dimension
AbstractLevel
ConcreteLevel
ImplementationLevel
Agents
Electronic Institutions (II)The OMNI framework
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Electronic Institutions (II)The OMNI framework
ROLE
ROLE
rolerelation
objectivesnorms
Social structure Interaction structure
SCENESCRIPT
SCENESCRIPT
player
landmarks
norms
resultsconstraints
scenetransition
OrganizationalOrganizational ModelModelNormative Normative Concrete Concrete LevelLevel
RoleNorms
SceneNorms Transition
Norms
Ontological Ontological Concrete Concrete LevelLevel
Ontologies
Communicationlanguages
Architectural Templates
RoleRules
SceneRules
TransitionRules
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Implementing Norms in eInstitutions (I)
• Implementation of norms from institutional perspective
• Implementation of a safe environment (norm enforcementnorm enforcement)• 2 options depending on control over agents
– Defining constraints on unwanted behaviour
– Defining violations and reacting to these violations
• our assumptions:– Norms can be sometimes violated by agents
– The internal state of agents is neither observable nor controlable
• actions cannot be imposed on an agent´s intentions
• agents as black boxes
• only their observable behaviour and actions
==Implementing a theorem prover to check protocol compliance
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Implementing Norms in eInstitutions (II)
• NormsNorms describe which states/actions within the e-organization should ideally take place
• NormsNorms are too abstract, no operational– A norm implementation norm implementation is composed by:
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Implementing Norms in eInstitutions (II)
• Norm enforcement is not centralized but distributed in a set of agents, the Police Agents– They check if a given (observable) action was legal or illegal
given the violation conditions defined for that context.
• The Agent Platform should assist the Police Agents, providing fast, very efficient aids for norm enforcement as additional platform services and mechanisms.
• A) Detection of the occurrence of an action– Police Agents may become overloaded checking ALL actions
– black list mechanism (of actions to monitor) e.g., assign
– action alarm mechanism (alarm to the Police Agent)
– The Police Agent checks if conditions for a violation apply.
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Implementing Norms in eInstitutions (III)
• B) Detection of activation/deactivation of norms– activation = when condition C is true
– deactivation = when P holds, A is done or C is false
– reaction time: time allowed between norm activation and reaction
– Depending on the complexity to check C, the platform should implement the apropriate fast-access data structures and/or processing mechanisms to reduce Police Agents´computation burden
• C) Deadline control– a clock trigger mechanism to detect that a deadline has passed
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Conclusions and Challenges
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• New Health Care services interconnnected in trans-national scenarios
• Need to explicitly handle the problem of – variety of regulations – trust, coordiantion and communication between agents of different systems
• Proposal of a language for norms
• Concept of normative agents. – Norms to define acceptable behaviour– Impact on the agent implementation
• Concept of Electronic Institutions– Norms to build a safe environment– Implementation of enforcement mechanisms
• Police Agents and platform services
Conclusions
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Challenges (I)
• Human trust on MAS technologies
• Creation of toolstools
ROLE
ROLE
rolerelation
objectivesnorms
Social structure Interaction structure
SCENESCRIPT
SCENESCRIPT
player
landmarks
norms
resultsconstraints
scenetransition
OrganizationalOrganizational ModelModelNormative Normative Concrete Concrete LevelLevel
RoleNorms
SceneNorms Transition
Norms
Ontological Ontological Concrete Concrete LevelLevel
Ontologies
Communicationlanguages
Architectural Templates
RoleRules
SceneRules
TransitionRules
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CbCa
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orgx
e-orgx
ai
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orgx
e-orgx
orgy
e-orgy
Cab
CbCa
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Cx
Challenges (II)
• Multi-level, multi-contextual ontologies
a) change of context b) consensus
c) colision in context definition
